Biomarker for early detection or diagnosis of sleep disorder in shift work and application of biomarker

Abstract

The invention provides application of biomarkers MPO, LTF and S100A8 in detection or diagnosis of shift work sleep disorders, application of a combination of MPO, LTF and S100A8 in preparation of products for detection or diagnosis of shift work sleep disorders, and related products. The combination of the biomarkers containing MPO, LTF and S100A8 provided by the invention is used for early diagnosis of sleep disorders in shift work, and has the advantages of high diagnosis accuracy and simple and quick method.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical detection, and particularly relates to biomarkers for early detection or diagnosis of shift work sleep disorder and their applications, especially applicable to the early screening and intervention of sleep disorder (SWD) in the shift work nurse group. Background Art

[0002] Sleep disorder has a significant impact on people's cognitive function and mood, and is prone to cause symptoms such as inattention, weakened alertness, memory decline, slow movement, slow reaction, auditory and visual hallucinations, irritability and mania. The harms brought by sleep disorder are related to various adverse health outcomes such as obesity, diabetes, mental health problems, heart disease and cancer. The incidence of sleep disorder in the shift work nurse group is as high as 30%-50%. Early diagnosis and intervention measures for shift work sleep disorder (SWD) are of great practical significance for the prevention and treatment of the disease, but there is currently a lack of specific diagnostic tools. In the prior art, the conventional diagnosis of SWD mostly relies on subjective questionnaires (such as PSQI) and polysomnography. These diagnostic detection methods often require patients to describe their medical history, long-term monitoring of sleep status, and can only be diagnosed when obvious deterioration of symptoms occurs, missing the best prevention and treatment window; and there are problems such as high detection cost, complex operation, and interference of subjective factors. Therefore, there is an urgent need to develop a simple, sensitive and clinically effective plasma biomarker detection method.

[0003] Myeloperoxidase (MPO) is a heme-containing enzyme mainly expressed in neutrophils. It was first discovered in myelocytes in 1940 and was named myeloperoxidase because of its abundant presence in myelocytes and peroxidase activity. MPO is a heme-containing glycoprotein composed of two heavy chains (molecular weight about 58kD) and two light chains (molecular weight about 14kD) connected by disulfide bonds, with a molecular weight of about 145kD. Currently, it is known that MPO catalyzes the reaction of chloride ions with hydrogen peroxide to produce hypochlorous acid (HOCl), and has a wide range of bactericidal abilities. However, the excessive production of MPO-derived oxidants has attracted people's attention to its harmful effects, especially in diseases characterized by acute or chronic inflammation. In cardiovascular diseases, MPO promotes the formation of foam cells by oxidizing low-density lipoprotein (LDL) to form oxidized LDL, accelerating the process of atherosclerosis; on the other hand, MPO can also directly damage vascular endothelial cells, leading to vascular dysfunction. In neurodegenerative diseases such as Alzheimer's disease, MPO can increase the toxicity and aggregation of β-amyloid (AB) deposits by oxidative modification, thereby accelerating neuronal damage and death. The role of MPO in cancer, lung diseases and kidney diseases has also attracted much attention.

[0004] S100A8 is a member of the S100 protein family, which binds calcium and zinc, consists of 93 amino acids, has a molecular weight of approximately 12 - 13 kDa, and is mainly expressed in neutrophils, monocytes, and tumor - associated fibroblasts. There are research reports that elevated serum S100A8 levels are associated with chemotherapy resistance in esophageal cancer and the active stage of systemic lupus erythematosus.

[0005] LTF (lactoferrin) is an iron - containing glycoprotein with antibacterial, immunomodulatory, and proteolytic activities. It can inhibit pathogen growth by chelating iron ions and play a bactericidal role by cleaving the arginine - rich sequence of microbial proteins. Currently, the research on LTF mainly focuses on autoimmune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) (see Soochow Univ etc., Extraordinarily potent proinflammatory properties of lactoferrin - containing immunocomplexes against human monocytes and macrophages, 2017, SCIENTIFIC REPORTS, 2045 - 2322, DOI: 10.1038 / s41598 - 017 - 04275 - 7).

[0006] Although there have been many studies on MPO, S100A8, and LTF currently, their roles in SWD have not been reported. Summary of the Invention

[0007] The inventors of the present invention have obtained a biomarker for the rapid diagnosis and / or intervention treatment of SWD by using proteomic screening techniques, through methods such as bioinformatics analysis, machine learning, in vitro verification, and animal models, filling the gap in the research and development of existing diagnostic markers.

[0008] First, in the first aspect of the present invention, there is provided an application of a reagent for detecting the protein level of a biomarker in a sample in the preparation of a product for detecting or diagnosing shift work sleep disorder (SWD), wherein the biomarker is MPO; preferably, the biomarker further includes LTF and S100A8.

[0009] More preferably, the reagent includes a kit for detecting the expression level of MPO; even more preferably, it further includes kits for detecting the expression levels of LTF and S100A8.

[0010] A kit for detecting the expression level of MPO, such as a probe specifically recognizing the mRNA of MPO; or, primers specifically recognizing the mRNA of MPO; or, an antibody or ligand specifically recognizing MPO. For example, using an MPO-specific antibody, detection is performed by enzyme-linked immunosorbent assay (ELISA).

[0011] A kit for detecting the expression level of LTF, such as a probe specifically recognizing the mRNA of LTF; or, primers specifically recognizing the mRNA of LTF; or, an antibody or ligand specifically recognizing LTF. For example, using an LTF-specific antibody, detection is performed by enzyme-linked immunosorbent assay (ELISA).

[0012] A kit for detecting the expression level of S100A8, such as a probe specifically recognizing the mRNA of S100A8; or, primers specifically recognizing the mRNA of S100A8; or, an antibody or ligand specifically recognizing S100A8. For example, using an S100A8-specific antibody, detection is performed by enzyme-linked immunosorbent assay (ELISA).

[0013] The kits including the kit for detecting the expression level of MPO, the kit for detecting the expression level of LTF, and the kit for detecting the expression level of S100A8 can be packaged separately or packaged together as a composition. Further, the detection is performed by ELISA.

[0014] In a preferred embodiment, the sample in the present invention is serum, plasma or blood; more preferably, the sample is from a human. The inventors found in a large number of investigations that the incidence of sleep disorder (SWD) in the group of shift-work nurses is as high as 30%-50%. Therefore, preferably, the sample source of the present invention is the group of shift-work nurses; further, the application of the reagent for detecting the biomarker protein level in the detection sample provided by the present invention in the preparation of a product for detecting or diagnosing shift-work sleep disorder is preferably used for the early diagnosis of sleep disorder in the group of shift-work nurses.

[0015] In another aspect of the present invention, there is also provided a combination of biomarkers for detecting or diagnosing SWD, and the biomarkers include a combination of MPO, LTF and S100A8.

[0016] In another aspect of the present invention, there is also provided the use of a combination of biomarkers MPO, LTF, and S100A8 in the preparation of a product for detecting or diagnosing SWD. Preferably, by detecting the expression level of MPO protein in a sample such as plasma, especially the expression levels of MPO, LTF, and S100A8 proteins in plasma, and then comparing with a reference, early identification and diagnosis of SWD patients can be carried out; specifically, when compared with the reference, if the level or content of the biomarker detected in the sample is up-regulated, it is determined that the source of the sample (such as a human) has sleep disorder or early sleep disorder.

[0017] In another aspect of the present invention, there is also provided a product, which comprises a reagent for detecting the level of biomarker protein in a sample, and the biomarkers are MPO, LTF, and S100A8.

[0018] Preferably, the reagent for detecting the level of biomarker protein in a sample is a reagent for detecting the expression level of MPO; more preferably, it further comprises reagents for detecting the expression levels of LTF and S100A8.

[0019] The reagents for detecting the expression levels of MPO, LTF, and S100A8 include but are not limited to probes that specifically recognize the mRNA of MPO, LTF, and S100A8; or primers that specifically recognize the mRNA of MPO, LTF, and S100A8; or antibodies or ligands that specifically recognize MPO, LTF, and S100A8. For example, using specific antibodies against MPO, LTF, and S100A8, detection can be carried out by enzyme-linked immunosorbent assay (ELISA).

[0020] In a preferred embodiment, the detection of the level of biomarker protein in the sample is carried out by ELISA.

[0021] Furthermore, the present invention also provides the use of the product in the preparation of a tool for diagnosing or detecting SWD. Especially the use in the preparation of a tool for early diagnosis or detection of SWD.

[0022] In another aspect of the present invention, there is also provided the use of an MPO-specific inhibitor in the preparation of a drug for improving neuroinflammation and anxiety and depressive states caused by SWD; preferably, the MPO-specific inhibitor is Verdiperstat.

[0023] As used in the context of the present invention, ELISA refers to detection using enzyme-linked immunosorbent assay. In enzyme-linked immunosorbent assay (ELISA), specific antibodies against MPO, LTF or S100A8 are adsorbed on the surface of a solid-phase carrier, enabling the enzyme-labeled antigen-antibody reaction to occur on the solid surface. This technique can be used to detect macromolecular antigens and specific antibodies, etc., and has the advantages of being rapid, sensitive, simple, and the carrier being easy to standardize.

[0024] Antibodies that specifically bind to MPO, LTF or S100A8 are known, and methods for preparation well-known in the art include, for example, generating polyclonal antibodies against MPO, LTF or S100A8 and monoclonal antibodies against specific fragments of MPO, LTF or S100A8. ELISA kits for MPO, LTF or S100A8 can also be obtained commercially, such as under the trade name Elabscience (purchased from Wuhan Elabs Biotechnology Co., Ltd.), etc.

[0025] The present inventors used high-throughput proteomics technology to identify and differentially quantify plasma samples from nurses with normal sleep, mild shift work sleep disorder (drowsiness), and shift work sleep disorder (drowsiness and insomnia). Through bioinformatics analysis, key biological change processes, signaling pathways, and molecules during the occurrence of SWD were obtained. Correlation analysis was performed between the obtained differential molecules and SWD scores (including drowsiness scores) to obtain key molecules that are correlated with SWD and continuously change during the progression of SWD disease. Then, the protein expression levels of MPO, LFT, and S100A8 in the plasma samples of the subjects were detected by ELISA in 12 normal individuals, 12 mild SWD patients, and 12 SWD patients, and the correlation between the protein expression levels of MPO, LFT, and S100A8 and the SWD scores was analyzed. Finally, machine learning and ROC curves were used to verify the diagnostic efficacy of the candidate molecules. After systematic analysis, three diagnostic markers, MPO, LTF, and S100A8, were finally obtained, with diagnostic AUC values of 0.88, 0.79, and 0.78 respectively, and the combined diagnostic AUC of the three markers being 0.94.

[0026] In further research, the present inventors used a specific MPO inhibitor (Verdiperstat) in a short-term sleep deprivation animal model to detect the anxiety and depressive behavior and neuroinflammatory indicators (TSPO PET, TNF-α, IL-6, hippocampal iba1 expression level) of mice. The results showed that targeting MPO could significantly improve the depressive and anxious mood and neuroinflammatory level in mice caused by sleep deprivation.

[0027] Accordingly, in a preferred embodiment, the present invention also provides a method for improving neuroinflammation and anxiety / depression states caused by SWD, the method comprising administering an MPO-specific inhibitor, such as Verdiperstat, to a patient with improved SWD.

[0028] In another embodiment, the present invention provides the use of an MPO-specific inhibitor, such as Verdiperstat, in the preparation of a medicament for improving neuroinflammation and anxiety / depression states caused by SWD.

[0029] The beneficial effects of the present invention are as follows:

[0030] The present invention for the first time provides the application that MPO, especially the combination of MPO, LTF and S100A83, can be used as a biomarker for SWD in serum, plasma or blood samples. The combination of MPO, LTF and S100A83 provided by the present invention is used as a biomarker in a sample for detection, for detecting or diagnosing SWD, with high accuracy, simple and fast method for early diagnosis of SWD.

[0031] In addition, the present invention further proposes that MPO is a potential target associated with SWD and depression, and it is demonstrated in a sleep deprivation animal model that oral administration of a targeted inhibitor of MPO can significantly improve the depressive state and neuroinflammation caused by sleep deprivation. The present invention for the first time proposes that MPO, especially the combination of MPO, LTF and S100A83, can be used as an early plasma biomarker and intervention punctuation for SWD. And by using a multi-omics approach, a variety of molecules consistent with disease progression have been screened out in normal people, patients with mild shift work sleep disorder (drowsiness), and patients with shift work sleep disorder (drowsiness and insomnia), verifying that MPO, especially the combination of MPO, LTF and S100A83, is a potential indicator for reflecting SWD and can be used as a monitoring indicator for disease treatment and prevention.

[0032] Term Explanation:

[0033] The present invention is intended to cover all alternative, modified and equivalent technical solutions, which are all included within the scope of the present invention as defined in the claims. Those skilled in the art should recognize that many methods and materials similar or equivalent to those described herein can be used to practice the present invention. The present invention is in no way limited to the methods and materials described herein.

[0034] Certain features of the present invention are described in multiple independent embodiments for clarity, but can also be provided in combination in a single embodiment. Conversely, various features of the present invention are described in a single embodiment for brevity, but can also be provided separately or in any suitable sub-combination.

[0035] The terms "comprising", "including", "having", "containing", or "involving" and other variant forms thereof in this text are inclusive or open-ended and do not exclude other unenumerated elements or method steps. That is, it includes what is specified in the present invention, but does not exclude other aspects.

[0036] The terms "selected from...", "preferably...", and "more preferably..." refer to one or more elements independently selected from the group listed hereinafter, and may include combinations of two or more elements. In the present invention, preferably one element from the group listed hereinafter is selected.

[0037] The terms "optionally", "optionally", or "optional" mean that the subsequent described event or situation may but does not necessarily occur, and this description includes the cases where the described event or situation occurs and the cases where it does not occur.

[0038] The term "ESS" is an abbreviation for Epworth Sleepiness Scale, and ESS is a commonly used scale for evaluating daytime sleepiness. The higher the score, the more severe the daytime sleepiness.

[0039] The term "ISS" is Insomnia Severity Index, and ISS is a scale for evaluating the severity of insomnia. The higher the score, the more severe the insomnia symptoms.

[0040] The term "Pre-SWD" is an abbreviation for Pre-Shift Work Disorder, referring to mild shift work sleep disorder;

[0041] The term "SWD" is an abbreviation for Shift Work Disorder, referring to shift work sleep disorder.

[0042] The term "mild shift work sleep disorder" refers to the early or subclinical stage of sleep problems, which is characterized by relatively mild symptoms and does not fully meet the clinical diagnostic criteria. It is mainly described as "excessive sleepiness", that is, the patient mainly shows strong drowsiness and easy to get sleepy during the day, but the insomnia symptoms at night may be relatively mild or not obvious.

[0043] The term "shift work sleep disorder" means that patients often show both daytime sleepiness and difficulty falling asleep or maintaining sleep at night.

[0044] The "mild shift work sleep disorder" and "shift work sleep disorder" mentioned above in the present invention can be classified according to the ESS (Epworth Sleepiness Scale) scoring standard and the ISS (Insomnia Severity Index) scoring standard.

[0045] Among them, the ESS is a scale used to evaluate the degree of daytime sleepiness. According to the 8 questions in the ESS scale, the subjects are evaluated. The score range for each question is from 0 to 3 points, and the total score is from 0 to 24 points.

[0046] The scoring criteria are as follows:

[0047] Scores of 1 - 6: Normal sleep

[0048] Scores of 7 - 8: General sleepiness

[0049] Scores of 9 - 24: Abnormal (possibly pathological) sleepiness.

[0050] The ISS is a scale used to evaluate the severity of insomnia. This scale contains 7 questions, and each question evaluates different aspects of insomnia, such as difficulty falling asleep, difficulty maintaining sleep, early awakening, etc. The score range for each question is from 0 to 4 points, and the total score is from 0 to 28 points. The scoring criteria are as follows:

[0051] Scores of 0 - 7: No insomnia, indicating good sleep quality.

[0052] Scores of 8 - 14: Mild insomnia, indicating the existence of minor sleep problems.

[0053] Scores of 15 - 21: Moderate insomnia, indicating the existence of moderate sleep problems.

[0054] Scores of 22 - 28: Severe insomnia, indicating the existence of serious sleep problems and may require treatment.

[0055] The "normal person" as described in the present invention refers to a subject who scores 0 - 6 according to the ESS scale and scores 0 - 7 according to the ISS scale;

[0056] The "mild shift work sleep disorder" as described in the present invention refers to a subject who scores ≥7 according to the ESS scale and scores 0 - 7 according to the ISS scale;

[0057] The "shift work sleep disorder" as described in the present invention refers to a subject who scores ≥7 according to the ESS scale and scores ≥8 according to the ISS scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Showing the protein clusters related to the progression of SWD disease screened by proteomics;

[0059] Among them Figure 1 A shows the clustering analysis of the differential protein expression heat map and disease progression, and the differential proteins in the progression of SWD can be roughly divided into 4 expression clustering patterns. Figure 1B shows the results of GO analysis (functional enrichment). Cluster 1 is related to inflammatory response and insulin signaling regulation. The genes involved include FGB, LTF, MPO, S100A8, S100A9, MSTN, IGFBP4, IGF1, and IGFALS.

[0060] Cluster 2 is related to focal adhesion. The genes involved include ITGA2B, COL6A1, CSPG4, ACTN1, ITGB3, and PDGFRB.

[0061] Cluster 3 is related to complement activation. The genes involved include STIP1, F13A1, HSP90B1, A2M, F12, and CCT6A.

[0062] Cluster 4 is related to microtubule-based process and carbon metabolism. The genes involved include YWHAQ, TUBA4A, EEF2, TUBA1A, HSPA8, HSPA4, ALDOA, SUCLA2, PGK1, MDH2, HNRNPA2B1, and YWHAG.

[0063] The colors of the heatmap range from blue to red, indicating the gene expression levels from low to high. The colors of the nodes in the functional enrichment analysis represent the gene expression levels in different clusters, with blue indicating low expression and red indicating high expression.

[0064] Figure 1 The results show that among the proteins in the four clustering patterns, the proteins in Cluster 1, which are consistent with disease progression, are mainly involved in inflammatory response and insulin signaling regulation.

[0065] Figure 2 It shows that MPO, LTF, and S100A8 are associated with immune, inflammatory, and oxidative stress metabolic processes.

[0066] Among them, Figure 2A shows the Venny (intersection) analysis of differential proteins between different degrees of shift work sleep disorder (Pre-SWD, -SWD) and normal people (control). The left red circle indicates that there are 113 genes in the comparison of "SWD vs Control" (SWD vs control group), and the right blue circle indicates that there are 30 genes in the comparison of "Pre-SWD vs Control" (Pre-SWD vs control group). The intersection part of the two circles has 32 genes, indicating genes with differential expression compared to the control group in both SWD and Pre-SWD. After KEGG (signaling pathway) analysis of the intersection proteins, it was found that most pathways are related to immunity, inflammation, and oxidative stress metabolism.

[0067] Figure 2 B shows the analysis of the expression levels of the intersection proteins in a heatmap. The left heatmap shows the expression of different genes in three comparisons: "Pre-SWD vs Control", "SWD vs Control", and "SWD vs Pre-SWD". The colors in the heatmap represent the high or low expression of genes, with red indicating high expression and blue indicating low expression. In the right heatmap, the color and size of each circle represent the strength and direction of the correlation, with red indicating a positive correlation and blue indicating a negative correlation. Figure 2 The results of B show that MPO, LTF, and S100A8 are also significantly differentially expressed in the comparison between SWD and Pre-SWD, and there is a significant positive correlation between the three and the excessive sleepiness score (ESS) and insomnia score (ISS), *p < 0.05;

[0068] Figure 3 It shows that the contents of MPO, LTF, and S100A8 in the plasma of patients with Pre-SWD and SWD are significantly higher than those in the plasma of normal subjects.

[0069] Among them Figure 3 A shows the detection of the levels of MPO, LTF, and S100A8 in the plasma of 12 normal people, 12 Pre-SWD patients, and 12 SWD patients by Elisa experiment. Figure 3 B shows the correlation analysis of Elisa data with the excessive sleepiness score (ESS) and insomnia score (ISS) of the corresponding samples. All data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, n = 12 for each group.

[0070] Figure 4 It shows that MPO is the preferred biomarker for differentiating SWD from normal people;

[0071] Among them Figure 4A shows the diagnostic efficacy of MPO, LTF, and S100A8 in distinguishing SWD from normal individuals using the area under the ROC curve (AUC) analysis, as well as the diagnostic efficacy after combining the three. The left figure shows the ROC curves and corresponding AUC values of individual indicators (MPO, S100A8, LTF). The AUC of MPO is 0.882, that of S100A8 is 0.788, and that of LTF is 0.792, indicating that MPO has the strongest diagnostic discrimination ability. The right figure shows the ROC curve of the combined indicator (MPO + S100A8 + LTF), and the AUC is increased to 0.938, indicating that the combined use of MPO + S100A8 + LTF can significantly improve the diagnostic or predictive efficacy.

[0072] Figure 4 B shows the evaluation of the specificity and accuracy rankings of MPO, LTF, and S100A8 in distinguishing SWD from normal individuals using the Random forest and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methods in machine learning. Among them, the MeanDecreaseAccuracy is the accuracy decline evaluation index.

[0073] Figure 5 It shows that oral administration of MPO inhibitor can significantly improve the depressive behavior and neuroinflammation of sleep-deprived mice.

[0074] Among them, Figure 5 A shows that Verdiperstat at a pre-protective dose of 50 mg / kg was administered to sleep-deprived mice, and then the locomotor and stationary states of the mice were tested by open field behavioral test. Figure 5 B shows the use of a neuroinflammation-specific in vivo tracer technique, TSPO PET, to detect the changes in neuroinflammation in different brain regions of mice after administration of MPO inhibitor. All data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, n = 4 per group.

[0075] Figure 6 It shows that targeting MPO inhibitor can significantly reduce the inflammatory level in the hippocampus of sleep-deprived mice.

[0076] Among them, Figure 6 A shows the detection of the levels of inflammatory factors TNF-α and IL-6 in the hippocampus and cortex of mice after administration of MPO inhibitor by Elisa. Figure 6 B shows the detection of the number of activated microglia in the mouse brain tissue, especially in the hippocampal tissue region, by Iba1 immunohistochemistry after administration. All data are expressed as mean ± SEM, *p < 0.05, n = 4 per group, scale bar = 200 μm.

[0077] In the above figures, Control refers to the normal group; Pre-SWD refers to the group of patients with mild shift work sleep disorder; SWD refers to the group of patients with shift work sleep disorder. Detailed implementation manners

[0078] The technical solutions and beneficial effects of the present invention will be further explained below in combination with specific embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0079] The sources of raw materials and related instruments are as follows:

[0080] Plasma samples of normal people, patients with mild shift work sleep disorder (Pre-SWD), and patients with shift work sleep disorder (SWD) are from Jiangyuan Hospital in Jiangsu Province.

[0081] Inclusion criteria for the experimental population: Female medical staff aged about 20 - 40 years old without other underlying diseases, with a complete basic questionnaire survey and easy to follow up; in good physical condition, with good eyesight, hearing and comprehension ability, and can cooperate to complete relevant interview work.

[0082] Exclusion criteria: Diabetes FPG > 126 mg / dL or HbA1C > 5.7%, hypertension (> 140 / 90 mmHg), hypertriglyceridemia > 4.0 μg / ml, smoking and drinking, liver and kidney dysfunction, suffering from chronic diseases, heart diseases, tumor cancers, other diseases, sensory disabilities (deafness, blindness), and sample breakage or loss during the experiment. These personnel, after signing the informed consent form, are investigated for sleep quality (drowsiness, insomnia) and depression mood scale (SDS) to improve the basic health information of the research population. Finally, 32 female personnel with an average age of about 33 years old are determined according to the inclusion and exclusion criteria. The experimental protocol has been approved by the Ethics Committee of Jiangsu Institute of Atomic Medicine.

[0083] Proteomics is detected by the protein detection platform of Shenzhen Center for Disease Control and Prevention.

[0084] The Elisa kits for MPO, LTF, S100A8, TNF-α and IL-6 are from Elabscience (Wuhan Elabs Biotechnology Co., Ltd.).

[0085] The preparation of the TSPO tracer ( 18 F-DPA714) is completed by the Molecular Imaging Center of Jiangsu Institute of Atomic Medicine, and PET imaging is performed using Micro-PET / MR 9.4T (Bruker). Other experimental reagents and instruments are all common reagents and equipment unless otherwise specified.

[0086] Plasma Proteomics Analysis of Patients with Shift Work Sleep Disorder in Example 1

[0087] In this example, plasma high-abundance protein removal, tandem mass tag (TMT) labeling, and liquid chromatography-tandem mass spectrometry (LC-MS / MS) strategies were used to analyze the plasma protein expression differences in the collected samples. Specifically, data were collected using a Q-Exactive mass spectrometer (Thermo Scientific, USA), and the obtained mass spectrometry data were searched in the UniProt-Homo sapiens database using Proteome Discoverer 2.1 software, followed by protein annotation and quantitative analysis. The protein false discovery rate (FDR) was set to 1%. The proteomics data were calculated using Perseus (http: / / www.coxdocs.org / ) software, and differential proteins were screened with a P value ≤ 0.05 and a Ratio value between 0.83 and 1.2. Mfuzz was used for disease progression clustering analysis, and for differential proteins, GO and KEGG enrichment analyses were performed using the Cluster Profiler (https: / / bioconductor.org / ) plugin of R language. The associations between differential protein signaling pathways were plotted using ClueGO.

[0088] After clustering analysis of the differential proteins screened by proteomics, the biological processes in which different cluster proteins were located were described. The results are as Figure 1 shown. Inflammatory response and insulin signal regulation were positively correlated with the disease progression of SWD (protein expression gradually increased).

[0089] Venny analysis was used to obtain proteins with significant differences in disease progression, and then KEGG signaling pathway analysis was performed to screen out differential molecules related to immune, inflammatory, and oxidative stress metabolic processes ( Figure 2 A). Finally, heat map expression analysis was performed on the obtained molecules, and it was proved that only MPO, LTF, and S100A8 had significant differences in the comparison between mild shift work sleep disorder (Pre-SWD) and shift work sleep disorder (SWD), and these three molecules were significantly positively correlated with the Epworth Sleepiness Scale (ESS) and the Insomnia Severity Index (ISS) ( Figure 2 B). The above proteomics analysis indicated that MPO, LTF, and S100A8 might be potential diagnostic markers.

[0090] Example 2 Verification of Biomarkers and Evaluation of Diagnostic Efficacy

[0091] The expression levels of MPO, LTF, and S100A8 in the collected plasma samples were detected by the Elisa method, and the expression of the above three markers in different groups was verified by differential analysis. The results showed that the contents of the three proteins, MPO, LTF, and S100A8, in the plasma of SWD subjects were significantly higher than those in the plasma of normal subjects, and the content of MPO was significantly increased in the plasma of both Pre-SWD and SWD patients ( Figure 3 A). Finally, the correlation analysis was performed between the expression levels of the 3 proteins and the ESS and ISS scores. The results showed that the correlation degree between MPO and ESS and ISS was the highest (r>0.5), indicating that MPO is a key biomarker for shift work sleep disorder in the nurse population.

[0092] To evaluate the diagnostic efficacy of the above three diagnostic markers, the area under the curve (AUC) was calculated by the ROC curve to evaluate their efficacy in diagnosing normal subjects and SWD. The results still showed that the AUC value of MPO was the largest, and the combination of the three markers could make the diagnostic efficacy AUC reach 0.94, higher than any single diagnostic marker ( Figure 4 A). Through further machine learning classification prediction evaluation, it was found that the diagnostic accuracy and error rate of MPO were the lowest in the random forest classification learning, and in another machine learning classification prediction model of SVM-RFE, MPO was the only biomarker selected by the error rate and diagnostic accuracy ( Figure 4 B). By means of machine learning, it was again shown that MPO is preferred and has high accuracy in diagnosing SWD.

[0093] The above screening, verification, and evaluation results of proteomics, Elisa verification, and machine learning diagnosis proved that the contents of MPO protein in the plasma of both Pre-SWD and SWD subjects were significantly higher than those in the plasma of normal subjects, proving that MPO is a potentially reliable early diagnostic biomarker for SWD; the combination of the three markers, MPO, LTF, and S100A8, can significantly improve the diagnostic efficacy.

[0094] Example 3 Verification of the reliability and applicability of the MPO target in an animal model

[0095] A short-term sleep deprivation model was used to simulate shift sleep disorder in the population. Preventive oral administration was carried out in normal mice for one week, and then sleep deprivation was performed, and behavioral tests and neuroinflammation were evaluated. Mainly using the MPO-targeted inhibitor (Verdiperstat, 50 mg / kg), oral administration was carried out during pre-protection and sleep deprivation, and then the open field experiment was used to evaluate the anxiety and depressive emotions of mice. The behavioral results of mice showed that the MPO inhibitor could significantly improve the anxiety and depressive emotions of sleep-deprived mice (the middle activity distance increased significantly, and the immobile time decreased significantly,Figure 5 A). Through PET imaging of TSPO neuroinflammation, it was found that the MPO inhibitor could significantly reduce the uptake value of the neuroinflammation imaging agent in the hippocampal region ( Figure 5 B).

[0096] The expression levels of TNF-α and IL-6 in the hippocampus and cortex regions of mice after sleep deprivation and drug administration were detected by Elisa, and the number of activated microglia in the mouse brain regions was detected by immunohistochemistry using a specific marker (Iba1) for microglia activation. The results showed that the MPO inhibitor could significantly reduce the expression levels of TNF-α and IL-6 in the hippocampal region of sleep-deprived mice and reduce the number of Iba1-positive cells in this region.

[0097] The results indicate that taking MPO as a pre-protective treatment target for sleep disorders can significantly improve the neuroinflammation and anxiety / depression states caused by sleep deprivation. Therefore, MPO can be used as a plasma biomarker for the early diagnosis of shift work sleep disorders in the population (such as nurses), and can also be used as an important target for the early prevention and treatment of SWD. The technical solution of the present invention will provide a new, reliable, accurate, convenient and rapid detection method for the early diagnosis of sleep disorders (SWD) in humans, such as nurses, and can also provide a possible treatment opportunity for SWD patients. Ultimately, it provides new technologies and solutions for the prevention and treatment of SWD.

[0098] The embodiments of the present disclosure are intended to elaborate on the technical solutions and beneficial effects of the present invention, and are not used to limit the protection scope of the present invention. Any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. Use of a reagent for detecting the level of a biomarker protein in a sample in the preparation of a product for detecting or diagnosing shift work sleep disorder, characterized in that, The biomarker is MPO.

2. The application according to claim 1, wherein The biomarker also includes LTF and S100A8.

3. The application according to claim 1, wherein, The reagent includes a kit for detecting the expression level of MPO, and preferably also includes kits for detecting the expression levels of LTF and S100A8.

4. The application according to claim 1, characterized in that The protein level of the biomarker in the test sample is detected by ELISA.

5. The application according to any one of claims 1 to 4, characterized in that, The sample is serum, plasma or blood; preferably the sample is from a human.

6. A combination of biomarkers for detecting or diagnosing shift work sleep disorder, characterized in that, The biomarker combination includes MPO, LTF and S100A8.

7. Use of the combination of MPO, LTF and S100A8 in the preparation of a product for detecting or diagnosing shift work sleep disorder.

8. A product, characterized in that, The product contains a reagent for detecting the protein level of the biomarker in the sample, and the biomarker is MPO, LTF and S100A8.

9. Use of the product according to claim 8 in the preparation of a tool for diagnosing or detecting shift work sleep disorder.

10. Use of an MPO specific inhibitor in the preparation of a drug for improving neuroinflammation and anxiety / depression states caused by shift work sleep disorder; preferably the MPO specific inhibitor is Verdiperstat.

Images